With a recent increase in available computational power, the field has seen a lot of progress with communication successfully emerging between reinforcement learning (RL) agents in a range of learning environments [1]}, [2]}. However, prior work shows that the emerged communication protocols often do not share properties of natural languages [3]} and that artificial agents tend towards an anti-efficient encoding [4]}. This likely happens because in many of the studied environments, communication is not situated—the action space of the agent does not include both communicative and environmental actions [1]}, [6]}. Agents do not learn to reason about whether to communicate; instead, communication is guaranteed and free to the agents. When free, excessive use of communication does not negatively affect the outcome of the game (or cause agent frustration), as it might in a real-world situation [7]}. As a possible solution, we explore situated communication and show it is possible to obtain concise communication protocols by providing the agent with an action-communication trade-off.
